Design,synthesis and preliminary activity evaluation of novel 3-amino-2-hydroxyl-3-phenylpropanoic acid derivatives as aminopeptidase N/CD13 inhibitors

Aminopeptidase N (APN/CD13) over expressed on tumour cells, plays a critical role in tumour invasion, metastasis and tumour angiogenesis. In this article, we described the design, synthesis and preliminary activity studies of novel 3-amino-2-hydroxyl-3-phenylpropanoic acid derivatives as APN inhibitors. The in vitro enzymatic inhibitions on APN from porcine kidney showed that compound 7e had the most potent inhibitory activity against APN with the IC₅₀ value to 1.26?±?0.01 μM, which is better than that of bestatin (IC₅₀?=?2.55?±?0.11 μM). In addition, compound 7e also showed better inhibitory activity against APN on human ovary clear cell carcinoma cell ES-2 than bestatin with the IC₅₀ value to 30.19?±?1.02 μM versus 60.61?±?0.1 μM. Compound 7e could be used as the lead compound in the future for anti-cancer agent research.     

Tailoring the Peptide-Binding Specificity Of An RNA by Combinations of Specificity-Altering Mutations

In this study, the ability to tailor the peptide-binding specificity of an RNA was investigated. First, variants of the Rev-response element (RRE) RNA with different specificities toward the natural binding partner, Rev, and two RRE-binding aptamers, the RSG-1.2 and the Kl peptides, were identified. Next, hybrid RRE mutants with combinations of two sets of specificity-altering substitutions were tested for peptide-binding specificity. It was shown that in most cases the results of the combination of individual mutations were of an additive nature, therefore providing a way to manipulate the peptide-binding specificity of an RNA in a predictable manner.     

Thermally Triggered Mucoadhesive In Situ Gel of Loratadine: β-Cyclodextrin Complex for Nasal Delivery

The aim of the present study was to increase the solubility of an anti-allergic drug loratadine by making its inclusion complex with β-cyclodextrin and to develop it’s thermally triggered mucoadhesive in situ nasal gel so as to overcome first-pass effect and consequently enhance its bioavailability. A total of eight formulations were prepared by cold method and optimized by 2³ full factorial design. Independent variables (concentration of poloxamer 407, concentration of carbopol 934 P, and pure drug or its inclusion complex) were optimized in order to achieve desired gelling temperature with sufficient mucoadhesive strength and maximum permeation across experimental nasal membrane. The design was validated by extra design checkpoint formulation (F9) and Pareto charts were used to help eliminate terms that did not have a statistically significant effect. The response surface plots and possible interactions between independent variables were analyzed using Design Expert Software 8.0.2 (Stat Ease, Inc., USA). Faster drug permeation with zero-order kinetics and target flux was achieved with formulation containing drug: β-cyclodextrin complex rather than those made with free drug. The optimized formulation (F8) with a gelling temperature of 28.6 ± 0.47°C and highest mucoadhesive strength of 7,676.0 ± 0.97 dyn/cm² displayed 97.74 ± 0.87% cumulative drug permeation at 6 h. It was stable for over 3 months and histological examination revealed no remarkable damage to the nasal tissue.     

Allosteric regulation of pentameric ligand-gated ion channels: An emerging mechanistic perspective

Pentameric ligand-gated ion channels (pLGICs) play a central role in intercellular communications in the nervous system by converting the binding of a chemical messenger—a neurotransmitter—into an ion flux through the postsynaptic membrane. They are oligomeric assemblies that provide prototypical examples of allosterically regulated integral membrane proteins. Here, we present an overview of the most recent advances on the signal transduction mechanism based on the X-ray structures of both prokaryotic and invertebrate eukaryotic pLGICs and on atomistic Molecular Dynamics simulations. The present results suggest that ion gating involves a large structural reorganization of the molecule mediated by two distinct quaternary transitions, a global twisting and the blooming of the extracellular domain, which can be modulated by ligand binding at the topographically distinct orthosteric and allosteric sites. The emerging model of gating is consistent with a wealth of functional studies and will boost the development of novel pharmacological strategies.     

IBC’s 23rd Annual Antibody Engineering, 10th Annual Antibody Therapeutics International Conferences and the 2012 Annual Meeting of The Antibody Society

The 23rd Annual Antibody Engineering, 10th Annual Antibody Therapeutics international conferences, and the 2012 Annual Meeting of The Antibody Society, organized by IBC Life Sciences with contributions from The Antibody Society and two Scientific Advisory Boards, were held December 3–6, 2012 in San Diego, CA. The meeting drew over 800 participants who attended sessions on a wide variety of topics relevant to antibody research and development. As a prelude to the main events, a pre-conference workshop held on December 2, 2012 focused on intellectual property issues that impact antibody engineering. The Antibody Engineering Conference was composed of six sessions held December 3–5, 2012: (1) From Receptor Biology to Therapy; (2) Antibodies in a Complex Environment; (3) Antibody Targeted CNS Therapy: Beyond the Blood Brain Barrier; (4) Deep Sequencing in B Cell Biology and Antibody Libraries; (5) Systems Medicine in the Development of Antibody Therapies/Systematic Validation of Novel Antibody Targets; and (6) Antibody Activity and Animal Models. The Antibody Therapeutics conference comprised four sessions held December 4–5, 2012: (1) Clinical and Preclinical Updates of Antibody-Drug Conjugates; (2) Multifunctional Antibodies and Antibody Combinations: Clinical Focus; (3) Development Status of Immunomodulatory Therapeutic Antibodies; and (4) Modulating the Half-Life of Antibody Therapeutics. The Antibody Society’s special session on applications for recording and sharing data based on GIATE was held on December 5, 2012, and the conferences concluded with two combined sessions on December 5–6, 2012: (1) Development Status of Early Stage Therapeutic Antibodies; and (2) Immunomodulatory Antibodies for Cancer Therapy.     

Nanomoulding of Functional Materials,a Versatile Complementary Pattern Replication Method to Nanoimprinting

We describe a nanomoulding technique which allows low-cost nanoscale patterning of functional materials, materials stacks and full devices. Nanomoulding combined with layer transfer enables the replication of arbitrary surface patterns from a master structure onto the functional material. Nanomoulding can be performed on any nanoimprinting setup and can be applied to a wide range of materials and deposition processes. In particular we demonstrate the fabrication of patterned transparent zinc oxide electrodes for light trapping applications in solar cells.     

Monoclonal antibodies in neuro-oncology

Monoclonal antibodies (mAbs) are used with increasing success against many tumors but, for brain tumors, the blood-brain barrier (BBB) is a special concern. The BBB prevents antibody entry to the normal brain; however, its role in brain tumor therapy is more complex. The BBB is closest to normal at micro-tumor sites; its properties and importance change as the tumor grows. In this review, evolving insight into the role of the BBB is balanced against other factors that affect efficacy or interpretation when mAbs are used against brain tumor targets. As specific examples, glioblastoma multiforme (GBM), primary central nervous system lymphoma (PCNSL) and blood-borne metastases from breast cancer are discussed in the context of treatment, respectively, with the mAbs bevacizumab, rituximab, and trastuzumab, each of which is already widely used against tumor outside the brain. It is suggested that success against brain tumors will require getting past the BBB in two senses: physically, to better attack brain tumor targets and conceptually, to give equal attention to problems that are shared with other tumor sites.     

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

ABSTRACT

Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug’s potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs’ sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.     

De novo discovery of antibody drugs – great promise demands scrutiny

ABSTRACT

We live in an era of rapidly advancing computing capacity and algorithmic sophistication. “Big data” and “artificial intelligence”find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts